| 集合变换卡尔曼滤波局地化对区域集合初始扰动的影响 |
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| 引用本文： | 马旭林,何佩仪,周勃旸,和杰.集合变换卡尔曼滤波局地化对区域集合初始扰动的影响[J].大气科学学报,2021,44(2):314-323. |
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| 作者姓名： | 马旭林 何佩仪 周勃旸 和杰 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室/资料同化研究与应用中心, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/资料同化研究与应用中心, 江苏 南京 210044;中国民航西南地区空中交通管理局气象中心, 四川 成都 610202;中国民用航空青岛空中交通管理站, 山东 青岛 266108 |
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| 基金项目： | 国家重点研发计划项目（2018YFC1506702；2017YFC1502000）；公益性行业（气象）科研专项（GYHY201506005） |
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| 摘 要： | 集合变换卡尔曼滤波(ensemble transform Kalman filter, ETKF)是一种有效的集合预报初始扰动构造方案。但是,有限的集合样本、相同的集合成员设置以及预报模式误差等可能会使两个距离较远的状态变量产生虚假相关,从而影响ETKF集合扰动的质量。为了有效解决远距离虚假相关问题,将局地化思想引入ETKF方案。本文针对GRAPES区域集合预报系统(GRAPES REPS),对ETKF初值扰动局地化方案的效果进行了试验分析,为进一步改善和优化局地化方案(LETKF方案)提供依据。通过一周的连续试验,从暴雨个例、集合预报多种评分检验等方面分析了LETKF初始扰动方案所产生的集合预报质量。结果表明,区域集合预报中集合变换卡尔曼滤波初始扰动的局地化方案能够更加合理地捕捉到快速增长的分析误差的物理结构,更准确地再现数值模式预报误差的线性与非线性传播和演变特征。该局地化方案可以较好地改进预报质量,提高降水预报的准确率,尤其是针对小雨、中雨、暴雨量级的预报。相对于现有区域集合预报的业务系统GRAPES REPS,基于局地化ETKF初始扰动方案的区域集合预报具有较明显的优势。总体来看,LETKF初始扰动方案可更好地改善区域集合预报的质量。
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| 关 键 词： | 集合预报 GRAPES 集合变换卡尔曼滤波 LETKF 初始扰动 |
| 收稿时间： | 2018/12/13 0:00:00 |
| 修稿时间： | 2019/2/7 0:00:00 |
Impact of localization of ensemble transform Kalman filter on initial perturbation of regional ensemble forecast |
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| MA Xulin,HE Peiyi,ZHOU Boyang,HE Jie.Impact of localization of ensemble transform Kalman filter on initial perturbation of regional ensemble forecast[J].大气科学学报,2021,44(2):314-323. |
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| Authors: | MA Xulin HE Peiyi ZHOU Boyang HE Jie |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Centre of Data Assimilation Research and Application, Nanjing University of Information Science and Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Centre of Data Assimilation Research and Application, Nanjing University of Information Science and Technology, Nanjing 210044, China;Meteorological Center of CAAC Southwest Air Traffic Administration, Chengdu 610202, China;Qingdao Air Traffic Management Station of Civil Aviation of China, Qingdao 266108, China |
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| Abstract: | The ensemble transform Kalman filter (ETKF) is an effective ensemble prediction initial perturbation scheme and is widely used.However,the finite ensemble sample size,the same ensemble member setting in ETKF and the forecast model error may make the two remote state variables have higher spurious correlation,thus affecting the quality of ETKF ensemble perturbation.The reason why ETKF generates spurious correlation is that each ensemble member is an estimate of the atmospheric state,while the degree of atmospheric freedom is too high,and the limited ensemble members are difficult to fully express.On the other hand,due to the effect of the forecast model error,the same size of members may lead to convergence of different ensemble members in the prediction process,resulting in spurious correlation.In order to solve this problem,the localization of ETKF,called LETKF,is proposed.By means of localization,the spurious correlation of error variance can be truncated in the localized radius,thus improving the quality of error variance.That is to say,only the observation data in the local radius are absorbed and aimed at a grid point,and the observation outside the radius is not taken into account so as to avoid the spurious correlation at a distance.Based on the GRAPES regional ensemble prediction system (GRAPES REPS),the localization scheme of ETKF initial perturbation is developed on the basis of the ETKF initial perturbation scheme,in order to solve the problem of the range spurious perturbation and the divergence of the filter in the regional ensemble prediction.Through the continuous experiments for 7 days,this paper analyzes the ensemble prediction quality of LETKF initial perturbation scheme from the case of rainstorm and multiple scoring methods of ensemble prediction.Results show that the localization scheme of ensemble transform Kalman filter initial perturbation in regional ensemble prediction can more reasonably capture the physical structure of the rapidly growing analysis error,and more accurately reproduce the linear and nonlinear propagation and evolution characteristics of the forecast error in the numerical model.The localization scheme can improve the quality of forecast and increase the accuracy of precipitation forecast,especially for the forecast of magnitude of light rain,moderate rain and rainstorm.Compared with the existing regional ensemble prediction business system GRAPES REPS,the regional ensemble prediction produced by LETKF initial perturbation scheme has obvious advantages.In general,the LETKF initial perturbation scheme can improve the quality of regional ensemble prediction. |
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| Keywords: | ensemble prediction GRAPES ensemble transform Kalman filter LETKF initial perturbation |
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